- P. A. Gutierrez24,
- S. Salcedo-Sanz25,
- M. J. Segovia-Vargas26,
- A. Sanchis27,
- J. A. Portilla-Figueras25,
- F. Fernández-Navarro24 &
- …
- C. Hervás-Martínez24
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Abstract
The financial system plays a crucial role in economic development. Financial crises are recurrent phenomena in modern financial systems. The literature offers several definitions of financial instability, but it is well known that a financial crisis with a banking crisis is the most common example of financial instability. In this paper we introduce a novel model for detection and prediction of crises, based on the hybridization of a standard logistic regression with Product Unit (PU) neural networks and Radial Basis Function (RBF) networks. These hybrid approaches are described in the paper, and applied to the detection and prediction of banking crises by using a large database of countries in the period 1981 to 1999. The proposed techniques are shown to perform better than other existing statistical and artificial intelligence methods for this problem.
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Authors and Affiliations
Universidad de Córdoba, Córdoba, Spain
P. A. Gutierrez, F. Fernández-Navarro & C. Hervás-Martínez
Universidad Complutense de Madrid, Madrid, Spain
S. Salcedo-Sanz & J. A. Portilla-Figueras
Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
M. J. Segovia-Vargas
Bank of Spain, Madrid, Spain
A. Sanchis
- P. A. Gutierrez
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Editors and Affiliations
Dept. of Computing and Numerical Analysis, University of Cordoba, Campus Universitario de Rabanales, Einstein Building, 3rd floor, 14071, Cordoba, Spain
Nicolás García-Pedrajas
Dept. of Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, 18071, Granada, Spain
Francisco Herrera
School of Computing, University of the West of Scotland, PA1 2BE, Paisley, UK
Colin Fyfe
Dept. Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, 18071, Granada, Spain
José Manuel Benítez
Department of Computer Science, Texas State University-San Marcos, 601 University Drive, TX 78666-4616, San Marcos, USA
Moonis Ali
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Gutierrez, P.A.et al. (2010). Generalized Logistic Regression Models Using Neural Network Basis Functions Applied to the Detection of Banking Crises. In: García-Pedrajas, N., Herrera, F., Fyfe, C., Benítez, J.M., Ali, M. (eds) Trends in Applied Intelligent Systems. IEA/AIE 2010. Lecture Notes in Computer Science(), vol 6098. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13033-5_1
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